US20070192213A1 - Feedback control theoretic parts inventory management model - Google Patents
Feedback control theoretic parts inventory management model Download PDFInfo
- Publication number
- US20070192213A1 US20070192213A1 US11/341,545 US34154506A US2007192213A1 US 20070192213 A1 US20070192213 A1 US 20070192213A1 US 34154506 A US34154506 A US 34154506A US 2007192213 A1 US2007192213 A1 US 2007192213A1
- Authority
- US
- United States
- Prior art keywords
- supply chain
- parts
- inventory
- conditions
- performance
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
- 238000000034 method Methods 0.000 claims abstract description 16
- 238000012544 monitoring process Methods 0.000 claims description 7
- 230000001934 delay Effects 0.000 claims description 4
- 230000007306 turnover Effects 0.000 claims description 4
- 238000009826 distribution Methods 0.000 claims description 2
- 238000001914 filtration Methods 0.000 claims 5
- 230000004931 aggregating effect Effects 0.000 claims 1
- 238000010586 diagram Methods 0.000 description 3
- 238000005457 optimization Methods 0.000 description 2
- 206010029216 Nervousness Diseases 0.000 description 1
- 238000004519 manufacturing process Methods 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 230000003442 weekly effect Effects 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/08—Logistics, e.g. warehousing, loading or distribution; Inventory or stock management
- G06Q10/087—Inventory or stock management, e.g. order filling, procurement or balancing against orders
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/08—Logistics, e.g. warehousing, loading or distribution; Inventory or stock management
Definitions
- OEMs original equipment manufacturers typically provide an after market parts supply to provide service vehicle parts for their vehicles.
- the service parts are ordered by and delivered to a dealer or a service shop that provides the parts to customers who wish their vehicles to be serviced. These parts typically follow a supply chain from the parts manufacturer to the vehicle owner.
- FIG. 1 shows a typical after market parts supply chain (PSC) 10 to provide after market service parts.
- the PSC 10 includes a supplier network 12 where suppliers manufacture or distribute the parts.
- the parts are warehoused in parts distribution centers (PDC) 14 , including parts processing centers (PPC), that are typically owned by the OEM for the vehicles it manufactures.
- PDC/PPC 14 typically is in the multi-echelon structure and provides parts to a certain portion of the market.
- a plurality of dealers or other automotive shops would be part of a dealer network 16 that would order parts from the PDC/PPC 14 .
- the dealer or vehicle shop in the network 16 would provide the parts and the vehicle service to customers 18 that own the vehicles.
- Known inventory planning policies typically determine the optimal inventory policy parameters for the multi-echelon PSC 10 on a monthly basis based on the inputs. Further, known inventory planning models are typically executed into the operational level of the PSC 10 without a real visibility of the PSC 10 , particularly, the actual supply chain conditions, such as inventory levels at the PDC/PPC 14 and the dealers, demand for the dealers and customers, transit time from one location to another in the PSC 10 , etc. However, it is very important to be able to track the actual condition as it often changes as a result of supply chain uncertainties, such as customer needs, parts usage life, transportation conditions, weather, etc. Moreover, the actual effectiveness of the PSC 10 is not explicitly monitored to measure the goodness of inventory planning. Therefore, improvements can be made in the inventory planning model to help better manage the parts supply chain and insure customer satisfaction and loyalty.
- a system and method for calibrating an inventory planning model used in a parts supply chain is disclosed.
- the inventory planning model determines the inventory policy parameters for parts inventory at a PDC/PPC based on certain inputs, such as order forecasts, transit times, processing/handling times, etc.
- the parts supply chain goes through the process of using parts to satisfy customer needs.
- a performance monitor measures the performance of the parts supply chain and provides performance metrics.
- the movement of parts through the parts supply chain is also monitored by a supply chain visibility system that keeps track of actual supply chain conditions.
- the information of both the actual supply chain conditions and the performance metrics is sent to a feedback filter that formats the information into an appropriate form.
- the filtered feedback is then sent to a feedback controller.
- the feedback controller determines how the input of the inventory planning model is adjusted, as well as how frequently the input is adjusted.
- FIG. 1 is a block diagram of a known vehicle parts supply chain
- FIG. 2 is a block diagram of a system for feedback control based on an inventory planning model for the parts supply chain shown in FIG. 1 .
- the present invention provides a feedback control based inventory planning model for an after market parts supply chain for service parts.
- the model leverages an existing supply chain visibility system to monitor actual supply chain conditions, tracks pre-specified performance metrics, and provides the appropriately filtered information to a feedback controller to effectively recalibrate the inventory targets.
- FIG. 2 is a block diagram of a system 22 for providing feedback control to an inventory planning (IP) model 24, according to an embodiment of the present invention.
- IP inventory planning
- the IP model 24 determines optimum inventory policy parameters based on predetermined inputs provided on line 34 to provide the desirable level of parts inventory.
- the inputs include order forecasts for dealers and end customers, nominal transit times, processing times, handling times, etc.
- the IP model 24 provides the inventory policy parameters, also discussed above, to the PSC 10 to control the inventory of parts through the PSC 10 .
- An input line 20 to the PSC 10 identifies the supply chain uncertainties inputs discussed above.
- the IP model 24 can be any multi-echelon inventory optimization model in the literature and practice.
- the system 22 includes a performance monitor (PM) 26 that receives predetermined variables from the PSC 10 to determine its performance by measuring predetermined performance metrics.
- the performance metrics include off the shelf parts availability at the PDC/PPC 14 and the dealers, the number of stock that is out, inventory turnover, expedited freight costs, generated revenue, etc.
- the PM 26 can be any suitable algorithm or other automated process that receives the variables from the PSC 10 , and uses that information to provide an output of the performance of the PSC 10 based on the predetermined performance metrics.
- the PSC variables from the PSC 10 are also provided to a supply chain visibility system (SCVS) 28 .
- the SCVS 28 keeps track of the actual supply chain conditions, such as inventory levels at the PDC/PPC 14 and the dealers, demand from the dealers and the end customers 18 , transit time from the supplier 12 to the PDC/PPC 14 or from the PDC/PPC 14 to the dealer 16 , etc. Further, the SCVS 28 keeps track of whether orders have been shipped, whether they have been received, any delays, etc.
- Various types of supply chain visibility systems are known in the art that can be used for this purpose.
- the actual supply chain conditions from the SCVS 28 and the performance metrics from the PM 26 are sent to a feedback filter processor 30 .
- the feedback filter processor 30 takes the performance metrics and actual conditions of the PCS 10 and puts the information into a desired format. For example, because some information, such as specific part shipments received by the feedback filter processor 30 , is updated every hours, the feedback filter processor 30 may aggregate the raw information into a daily or weekly basis. Further, because various supply chain visibility systems can be used within the system 22 , the feedback filter processor 30 can put the information from the SCVS 28 into the proper format.
- the feedback filter processor 30 can also be any suitable algorithm or other automated process that is designed to take the input discussed above and provide a quantified output of the operation of the PSC 10 .
- the properly filtered information on supply chain conditions and performance metrics from the feedback filter processor 30 is then sent to a feedback controller 32 that also receives the inputs on the line 34 .
- the feedback controller 32 determines how the input of the IP model 24 is adjusted, as well as how frequently the input is adjusted based on the filtered feedback information and the predetermined inputs.
- the feedback controller 32 can employ any model from feedback control theory suitable for the purposes described herein.
- the feedback controller 32 can include certain software that has been programmed that takes the input of the filtered feedback to adjust the input of the IP model 24 to set the inventory policy parameters. As the system 22 cycles and the filtered feedback is provided to the feedback controller 32 , the performance of the PSC 10 should stay around the desired performance target. A threshold value can be specified so that recalibration can be guided to effectively respond to an exceptional alert without causing unnecessary system nervousness and noise.
Abstract
Description
- 1. Field of the Invention
- This invention relates generally to a system and method for providing inventory planning for a service parts supply chain, where inventory polices are established based on multi-echelon inventory planning optimization while using a performance monitor for measuring the performance of the supply chain, a supply chain visibility system for monitoring actual conditions of the supply chain and a feedback controller for adjusting the input of the inventory planning and recalibrate it based on the actual performance and conditions of the supply chain.
- 2. Discussion of the Related Art
- OEMs (original equipment manufacturers) typically provide an after market parts supply to provide service vehicle parts for their vehicles. The service parts are ordered by and delivered to a dealer or a service shop that provides the parts to customers who wish their vehicles to be serviced. These parts typically follow a supply chain from the parts manufacturer to the vehicle owner.
-
FIG. 1 shows a typical after market parts supply chain (PSC) 10 to provide after market service parts. The PSC 10 includes asupplier network 12 where suppliers manufacture or distribute the parts. The parts are warehoused in parts distribution centers (PDC) 14, including parts processing centers (PPC), that are typically owned by the OEM for the vehicles it manufactures. The PDC/PPC 14 typically is in the multi-echelon structure and provides parts to a certain portion of the market. A plurality of dealers or other automotive shops would be part of adealer network 16 that would order parts from the PDC/PPC 14. The dealer or vehicle shop in thenetwork 16 would provide the parts and the vehicle service tocustomers 18 that own the vehicles. - It is critical for the OEM to provide the right quantity of the right parts at the right place at the right time to ensure customer satisfaction and loyalty. To accomplish these tasks, the OEM will employ an inventory planning model to effectively manage the inventory at PDC/
PPC 14. The inventory planning model determines optimum inventory policy parameters based on predetermined inputs, such as order forecasts for dealers and end customers, nominal transit times, processing times, handling times, etc., for stocking the parts at the PDC/PPC 14. Depending on various inventory policies, inventory policy parameters include safety stock level, minimum and maximum inventory levels, inventory re-order points, order-up-to inventory level, etc. - Known inventory planning policies typically determine the optimal inventory policy parameters for the
multi-echelon PSC 10 on a monthly basis based on the inputs. Further, known inventory planning models are typically executed into the operational level of thePSC 10 without a real visibility of thePSC 10, particularly, the actual supply chain conditions, such as inventory levels at the PDC/PPC 14 and the dealers, demand for the dealers and customers, transit time from one location to another in thePSC 10, etc. However, it is very important to be able to track the actual condition as it often changes as a result of supply chain uncertainties, such as customer needs, parts usage life, transportation conditions, weather, etc. Moreover, the actual effectiveness of thePSC 10 is not explicitly monitored to measure the goodness of inventory planning. Therefore, improvements can be made in the inventory planning model to help better manage the parts supply chain and insure customer satisfaction and loyalty. - In accordance with the teachings of the present invention, a system and method for calibrating an inventory planning model used in a parts supply chain is disclosed. The inventory planning model determines the inventory policy parameters for parts inventory at a PDC/PPC based on certain inputs, such as order forecasts, transit times, processing/handling times, etc. The parts supply chain goes through the process of using parts to satisfy customer needs. A performance monitor measures the performance of the parts supply chain and provides performance metrics. The movement of parts through the parts supply chain is also monitored by a supply chain visibility system that keeps track of actual supply chain conditions. The information of both the actual supply chain conditions and the performance metrics is sent to a feedback filter that formats the information into an appropriate form. The filtered feedback is then sent to a feedback controller. Along with the predetermined input, the feedback controller determines how the input of the inventory planning model is adjusted, as well as how frequently the input is adjusted.
- Additional features of the present invention will become apparent from the following description and appended claims, taken in conjunction with the accompanying drawings.
-
FIG. 1 is a block diagram of a known vehicle parts supply chain; and -
FIG. 2 is a block diagram of a system for feedback control based on an inventory planning model for the parts supply chain shown inFIG. 1 . - The following discussion of the embodiments of the invention for a feedback control based inventory planning model of a parts supply chain is merely exemplary in nature, and is in no way intended to limit the invention or its applications or uses.
- As will be discussed in detail below, the present invention provides a feedback control based inventory planning model for an after market parts supply chain for service parts. The model leverages an existing supply chain visibility system to monitor actual supply chain conditions, tracks pre-specified performance metrics, and provides the appropriately filtered information to a feedback controller to effectively recalibrate the inventory targets.
-
FIG. 2 is a block diagram of asystem 22 for providing feedback control to an inventory planning (IP)model 24, according to an embodiment of the present invention. As discussed above, theIP model 24 determines optimum inventory policy parameters based on predetermined inputs provided online 34 to provide the desirable level of parts inventory. The inputs include order forecasts for dealers and end customers, nominal transit times, processing times, handling times, etc. TheIP model 24 provides the inventory policy parameters, also discussed above, to thePSC 10 to control the inventory of parts through thePSC 10. Aninput line 20 to thePSC 10 identifies the supply chain uncertainties inputs discussed above. TheIP model 24 can be any multi-echelon inventory optimization model in the literature and practice. - Various aspects of the
PSC 10 can be monitored to give a quantified depiction of the flow of parts through thePSC 10. According to the invention, thesystem 22 includes a performance monitor (PM) 26 that receives predetermined variables from thePSC 10 to determine its performance by measuring predetermined performance metrics. In one embodiment, the performance metrics include off the shelf parts availability at the PDC/PPC 14 and the dealers, the number of stock that is out, inventory turnover, expedited freight costs, generated revenue, etc. ThePM 26 can be any suitable algorithm or other automated process that receives the variables from thePSC 10, and uses that information to provide an output of the performance of thePSC 10 based on the predetermined performance metrics. - The PSC variables from the
PSC 10 are also provided to a supply chain visibility system (SCVS) 28. The SCVS 28 keeps track of the actual supply chain conditions, such as inventory levels at the PDC/PPC 14 and the dealers, demand from the dealers and theend customers 18, transit time from thesupplier 12 to the PDC/PPC 14 or from the PDC/PPC14 to thedealer 16, etc. Further, the SCVS 28 keeps track of whether orders have been shipped, whether they have been received, any delays, etc. Various types of supply chain visibility systems are known in the art that can be used for this purpose. - The actual supply chain conditions from the
SCVS 28 and the performance metrics from thePM 26 are sent to afeedback filter processor 30. Thefeedback filter processor 30 takes the performance metrics and actual conditions of thePCS 10 and puts the information into a desired format. For example, because some information, such as specific part shipments received by thefeedback filter processor 30, is updated every hours, thefeedback filter processor 30 may aggregate the raw information into a daily or weekly basis. Further, because various supply chain visibility systems can be used within thesystem 22, thefeedback filter processor 30 can put the information from theSCVS 28 into the proper format. Thefeedback filter processor 30 can also be any suitable algorithm or other automated process that is designed to take the input discussed above and provide a quantified output of the operation of thePSC 10. - The properly filtered information on supply chain conditions and performance metrics from the
feedback filter processor 30 is then sent to afeedback controller 32 that also receives the inputs on theline 34. Thefeedback controller 32 determines how the input of theIP model 24 is adjusted, as well as how frequently the input is adjusted based on the filtered feedback information and the predetermined inputs. Thefeedback controller 32 can employ any model from feedback control theory suitable for the purposes described herein. Thefeedback controller 32 can include certain software that has been programmed that takes the input of the filtered feedback to adjust the input of theIP model 24 to set the inventory policy parameters. As thesystem 22 cycles and the filtered feedback is provided to thefeedback controller 32, the performance of thePSC 10 should stay around the desired performance target. A threshold value can be specified so that recalibration can be guided to effectively respond to an exceptional alert without causing unnecessary system nervousness and noise. - The foregoing discussion discloses and describes merely exemplary embodiments of the present invention. One skilled in the art will readily recognize from such discussion and from the accompanying drawings and claims that various changes, modifications and variations can be made therein without departing from the spirit and scope of the invention as defined in the following claims.
Claims (20)
Priority Applications (1)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
US11/341,545 US8473373B2 (en) | 2006-01-27 | 2006-01-27 | Feedback control theoretic parts inventory management model |
Applications Claiming Priority (1)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
US11/341,545 US8473373B2 (en) | 2006-01-27 | 2006-01-27 | Feedback control theoretic parts inventory management model |
Publications (2)
Publication Number | Publication Date |
---|---|
US20070192213A1 true US20070192213A1 (en) | 2007-08-16 |
US8473373B2 US8473373B2 (en) | 2013-06-25 |
Family
ID=38369892
Family Applications (1)
Application Number | Title | Priority Date | Filing Date |
---|---|---|---|
US11/341,545 Expired - Fee Related US8473373B2 (en) | 2006-01-27 | 2006-01-27 | Feedback control theoretic parts inventory management model |
Country Status (1)
Country | Link |
---|---|
US (1) | US8473373B2 (en) |
Cited By (23)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US20080208659A1 (en) * | 2005-04-29 | 2008-08-28 | Lianjun An | Method and Apparatus Combining control Theory and Business Performance Management |
US20120010919A1 (en) * | 2009-03-09 | 2012-01-12 | Abhilasha Aswal | New vistas in inventory optimization under uncertainty |
US8428985B1 (en) * | 2009-09-04 | 2013-04-23 | Ford Motor Company | Multi-feature product inventory management and allocation system and method |
US20130211870A1 (en) * | 2012-02-09 | 2013-08-15 | Rockwell Automation Technologies, Inc. | Real-time tracking of product using a cloud platform |
US8626327B2 (en) | 2010-11-05 | 2014-01-07 | The Coca-Cola Company | System for optimizing drink blends |
US8626564B2 (en) | 2010-11-05 | 2014-01-07 | The Coca-Cola Company | System and method for simulating drink production |
US8639374B2 (en) | 2010-11-05 | 2014-01-28 | The Coca-Cola Company | Method, apparatus and system for regulating a product attribute profile |
US9363336B2 (en) | 2012-02-09 | 2016-06-07 | Rockwell Automation Technologies, Inc. | Smart device for industrial automation |
US9438648B2 (en) | 2013-05-09 | 2016-09-06 | Rockwell Automation Technologies, Inc. | Industrial data analytics in a cloud platform |
US9703902B2 (en) | 2013-05-09 | 2017-07-11 | Rockwell Automation Technologies, Inc. | Using cloud-based data for industrial simulation |
US9709978B2 (en) | 2013-05-09 | 2017-07-18 | Rockwell Automation Technologies, Inc. | Using cloud-based data for virtualization of an industrial automation environment with information overlays |
US9786197B2 (en) | 2013-05-09 | 2017-10-10 | Rockwell Automation Technologies, Inc. | Using cloud-based data to facilitate enhancing performance in connection with an industrial automation system |
US20180121874A1 (en) * | 2016-11-03 | 2018-05-03 | Flexport, Inc. | Method and system for supply chain management |
US9989958B2 (en) | 2013-05-09 | 2018-06-05 | Rockwell Automation Technologies, Inc. | Using cloud-based data for virtualization of an industrial automation environment |
US10026049B2 (en) | 2013-05-09 | 2018-07-17 | Rockwell Automation Technologies, Inc. | Risk assessment for industrial systems using big data |
US10496061B2 (en) | 2015-03-16 | 2019-12-03 | Rockwell Automation Technologies, Inc. | Modeling of an industrial automation environment in the cloud |
US11042131B2 (en) | 2015-03-16 | 2021-06-22 | Rockwell Automation Technologies, Inc. | Backup of an industrial automation plant in the cloud |
CN113850448A (en) * | 2021-12-01 | 2021-12-28 | 广东智修互联大数据有限公司 | Management method and system for consumption and scheduling of spare parts |
US11243505B2 (en) | 2015-03-16 | 2022-02-08 | Rockwell Automation Technologies, Inc. | Cloud-based analytics for industrial automation |
WO2022047696A1 (en) * | 2020-09-03 | 2022-03-10 | Boe Technology Group Co., Ltd. | Intelligent management system, intelligent management method, and computer-program product |
US11513477B2 (en) | 2015-03-16 | 2022-11-29 | Rockwell Automation Technologies, Inc. | Cloud-based industrial controller |
EP4134889A1 (en) | 2021-08-11 | 2023-02-15 | Implement Consulting Group P/S | A method and a system for customer demand driven supply chain planning |
CN117114583A (en) * | 2023-10-24 | 2023-11-24 | 电能易购(北京)科技有限公司 | Supply chain management system based on cloud service platform |
Families Citing this family (1)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US9779381B1 (en) * | 2011-12-15 | 2017-10-03 | Jda Software Group, Inc. | System and method of simultaneous computation of optimal order point and optimal order quantity |
Citations (8)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US20020103709A1 (en) * | 2001-01-31 | 2002-08-01 | Anthony Richard M. | Closed loop demand fulfillment system and method |
US20020165804A1 (en) * | 2001-05-01 | 2002-11-07 | Beebe Melissa D. | Automated data warehouse for demand fulfillment system |
US20020188499A1 (en) * | 2000-10-27 | 2002-12-12 | Manugistics, Inc. | System and method for ensuring order fulfillment |
US20030083947A1 (en) * | 2001-04-13 | 2003-05-01 | Hoffman George Harry | System, method and computer program product for governing a supply chain consortium in a supply chain management framework |
US20030216952A1 (en) * | 2002-05-17 | 2003-11-20 | Robert Duncan Klett | System and method for determining a promise date for a demand in a business environment |
US7292904B2 (en) * | 2003-10-31 | 2007-11-06 | International Business Machines Corporation | Method for sizing production lot starts within a linear system programming environment |
US20080040183A1 (en) * | 2000-10-26 | 2008-02-14 | Birjandi Rosa H | Optimized Deployment of Parts in a Distribution Network |
US20080147490A1 (en) * | 2000-10-26 | 2008-06-19 | Adeel Najmi | Optimized Deployment of Parts in a Supply Chain Network |
-
2006
- 2006-01-27 US US11/341,545 patent/US8473373B2/en not_active Expired - Fee Related
Patent Citations (10)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US20080040183A1 (en) * | 2000-10-26 | 2008-02-14 | Birjandi Rosa H | Optimized Deployment of Parts in a Distribution Network |
US20080046309A1 (en) * | 2000-10-26 | 2008-02-21 | Birjandi Rosa H | Optimized Deployment of Parts in a Distribution Network |
US20080147490A1 (en) * | 2000-10-26 | 2008-06-19 | Adeel Najmi | Optimized Deployment of Parts in a Supply Chain Network |
US20020188499A1 (en) * | 2000-10-27 | 2002-12-12 | Manugistics, Inc. | System and method for ensuring order fulfillment |
US20020103709A1 (en) * | 2001-01-31 | 2002-08-01 | Anthony Richard M. | Closed loop demand fulfillment system and method |
US6920427B2 (en) * | 2001-01-31 | 2005-07-19 | Dell Products L.P. | Closed loop demand fulfillment system and method |
US20030083947A1 (en) * | 2001-04-13 | 2003-05-01 | Hoffman George Harry | System, method and computer program product for governing a supply chain consortium in a supply chain management framework |
US20020165804A1 (en) * | 2001-05-01 | 2002-11-07 | Beebe Melissa D. | Automated data warehouse for demand fulfillment system |
US20030216952A1 (en) * | 2002-05-17 | 2003-11-20 | Robert Duncan Klett | System and method for determining a promise date for a demand in a business environment |
US7292904B2 (en) * | 2003-10-31 | 2007-11-06 | International Business Machines Corporation | Method for sizing production lot starts within a linear system programming environment |
Cited By (53)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US10018993B2 (en) | 2002-06-04 | 2018-07-10 | Rockwell Automation Technologies, Inc. | Transformation of industrial data into useful cloud information |
US8626544B2 (en) * | 2005-04-29 | 2014-01-07 | International Business Machines Corporation | Method and apparatus combining control theory and business performance management |
US20080208659A1 (en) * | 2005-04-29 | 2008-08-28 | Lianjun An | Method and Apparatus Combining control Theory and Business Performance Management |
US20120010919A1 (en) * | 2009-03-09 | 2012-01-12 | Abhilasha Aswal | New vistas in inventory optimization under uncertainty |
US8428985B1 (en) * | 2009-09-04 | 2013-04-23 | Ford Motor Company | Multi-feature product inventory management and allocation system and method |
US11048237B2 (en) | 2010-11-05 | 2021-06-29 | The Coca-Cola Company | System for optimizing drink blends |
US8626327B2 (en) | 2010-11-05 | 2014-01-07 | The Coca-Cola Company | System for optimizing drink blends |
US8626564B2 (en) | 2010-11-05 | 2014-01-07 | The Coca-Cola Company | System and method for simulating drink production |
US8639374B2 (en) | 2010-11-05 | 2014-01-28 | The Coca-Cola Company | Method, apparatus and system for regulating a product attribute profile |
US20140121802A1 (en) * | 2010-11-05 | 2014-05-01 | The Coca-Cola Company | System for optimizing drink blends |
US10762247B2 (en) | 2010-11-05 | 2020-09-01 | The Coca-Cola Company | System and method of producing a multi component product |
US10261501B2 (en) * | 2010-11-05 | 2019-04-16 | The Coca-Cola Company | System for optimizing drink blends |
US10139811B2 (en) | 2012-02-09 | 2018-11-27 | Rockwell Automation Technologies, Inc. | Smart device for industrial automation |
US10749962B2 (en) | 2012-02-09 | 2020-08-18 | Rockwell Automation Technologies, Inc. | Cloud gateway for industrial automation information and control systems |
US9568909B2 (en) | 2012-02-09 | 2017-02-14 | Rockwell Automation Technologies, Inc. | Industrial automation service templates for provisioning of cloud services |
US9568908B2 (en) | 2012-02-09 | 2017-02-14 | Rockwell Automation Technologies, Inc. | Industrial automation app-store |
US11470157B2 (en) | 2012-02-09 | 2022-10-11 | Rockwell Automation Technologies, Inc. | Cloud gateway for industrial automation information and control systems |
US20130211870A1 (en) * | 2012-02-09 | 2013-08-15 | Rockwell Automation Technologies, Inc. | Real-time tracking of product using a cloud platform |
US10965760B2 (en) | 2012-02-09 | 2021-03-30 | Rockwell Automation Technologies, Inc. | Cloud-based operator interface for industrial automation |
US9363336B2 (en) | 2012-02-09 | 2016-06-07 | Rockwell Automation Technologies, Inc. | Smart device for industrial automation |
US9565275B2 (en) | 2012-02-09 | 2017-02-07 | Rockwell Automation Technologies, Inc. | Transformation of industrial data into useful cloud information |
US9965562B2 (en) | 2012-02-09 | 2018-05-08 | Rockwell Automation Technologies, Inc. | Industrial automation app-store |
US9413852B2 (en) | 2012-02-09 | 2016-08-09 | Rockwell Automation Technologies, Inc. | Time-stamping of industrial cloud data for synchronization |
US9477936B2 (en) | 2012-02-09 | 2016-10-25 | Rockwell Automation Technologies, Inc. | Cloud-based operator interface for industrial automation |
US10116532B2 (en) | 2012-02-09 | 2018-10-30 | Rockwell Automation Technologies, Inc. | Cloud-based operator interface for industrial automation |
US9709978B2 (en) | 2013-05-09 | 2017-07-18 | Rockwell Automation Technologies, Inc. | Using cloud-based data for virtualization of an industrial automation environment with information overlays |
US11295047B2 (en) | 2013-05-09 | 2022-04-05 | Rockwell Automation Technologies, Inc. | Using cloud-based data for industrial simulation |
US10204191B2 (en) | 2013-05-09 | 2019-02-12 | Rockwell Automation Technologies, Inc. | Using cloud-based data for industrial simulation |
US10257310B2 (en) | 2013-05-09 | 2019-04-09 | Rockwell Automation Technologies, Inc. | Industrial data analytics in a cloud platform |
US9989958B2 (en) | 2013-05-09 | 2018-06-05 | Rockwell Automation Technologies, Inc. | Using cloud-based data for virtualization of an industrial automation environment |
US10984677B2 (en) | 2013-05-09 | 2021-04-20 | Rockwell Automation Technologies, Inc. | Using cloud-based data for industrial automation system training |
US10564633B2 (en) | 2013-05-09 | 2020-02-18 | Rockwell Automation Technologies, Inc. | Using cloud-based data for virtualization of an industrial automation environment with information overlays |
US10726428B2 (en) | 2013-05-09 | 2020-07-28 | Rockwell Automation Technologies, Inc. | Industrial data analytics in a cloud platform |
US11676508B2 (en) | 2013-05-09 | 2023-06-13 | Rockwell Automation Technologies, Inc. | Using cloud-based data for industrial automation system training |
US9954972B2 (en) | 2013-05-09 | 2018-04-24 | Rockwell Automation Technologies, Inc. | Industrial data analytics in a cloud platform |
US10816960B2 (en) | 2013-05-09 | 2020-10-27 | Rockwell Automation Technologies, Inc. | Using cloud-based data for virtualization of an industrial machine environment |
US9786197B2 (en) | 2013-05-09 | 2017-10-10 | Rockwell Automation Technologies, Inc. | Using cloud-based data to facilitate enhancing performance in connection with an industrial automation system |
US9703902B2 (en) | 2013-05-09 | 2017-07-11 | Rockwell Automation Technologies, Inc. | Using cloud-based data for industrial simulation |
US9438648B2 (en) | 2013-05-09 | 2016-09-06 | Rockwell Automation Technologies, Inc. | Industrial data analytics in a cloud platform |
US10026049B2 (en) | 2013-05-09 | 2018-07-17 | Rockwell Automation Technologies, Inc. | Risk assessment for industrial systems using big data |
US11513477B2 (en) | 2015-03-16 | 2022-11-29 | Rockwell Automation Technologies, Inc. | Cloud-based industrial controller |
US11243505B2 (en) | 2015-03-16 | 2022-02-08 | Rockwell Automation Technologies, Inc. | Cloud-based analytics for industrial automation |
US11409251B2 (en) | 2015-03-16 | 2022-08-09 | Rockwell Automation Technologies, Inc. | Modeling of an industrial automation environment in the cloud |
US11927929B2 (en) | 2015-03-16 | 2024-03-12 | Rockwell Automation Technologies, Inc. | Modeling of an industrial automation environment in the cloud |
US11042131B2 (en) | 2015-03-16 | 2021-06-22 | Rockwell Automation Technologies, Inc. | Backup of an industrial automation plant in the cloud |
US11880179B2 (en) | 2015-03-16 | 2024-01-23 | Rockwell Automation Technologies, Inc. | Cloud-based analytics for industrial automation |
US10496061B2 (en) | 2015-03-16 | 2019-12-03 | Rockwell Automation Technologies, Inc. | Modeling of an industrial automation environment in the cloud |
US11080652B2 (en) * | 2016-11-03 | 2021-08-03 | Flexport, Inc. | Method and system for supply chain management |
US20180121874A1 (en) * | 2016-11-03 | 2018-05-03 | Flexport, Inc. | Method and system for supply chain management |
WO2022047696A1 (en) * | 2020-09-03 | 2022-03-10 | Boe Technology Group Co., Ltd. | Intelligent management system, intelligent management method, and computer-program product |
EP4134889A1 (en) | 2021-08-11 | 2023-02-15 | Implement Consulting Group P/S | A method and a system for customer demand driven supply chain planning |
CN113850448A (en) * | 2021-12-01 | 2021-12-28 | 广东智修互联大数据有限公司 | Management method and system for consumption and scheduling of spare parts |
CN117114583A (en) * | 2023-10-24 | 2023-11-24 | 电能易购(北京)科技有限公司 | Supply chain management system based on cloud service platform |
Also Published As
Publication number | Publication date |
---|---|
US8473373B2 (en) | 2013-06-25 |
Similar Documents
Publication | Publication Date | Title |
---|---|---|
US8473373B2 (en) | Feedback control theoretic parts inventory management model | |
US20090112672A1 (en) | Method and Arrangement for Optimized Maintenance of Components | |
US9015059B2 (en) | Wireless system for automatic ordering of maintenance parts for equipment | |
US20060276185A1 (en) | Wireless system for providing critical sensor alerts for equipment | |
US8265986B2 (en) | System and method for determining carbon emission-conscious order fulfillment alternatives with multiple supply modes | |
US20050159973A1 (en) | Method and system for computerizing quality management of a supply chain | |
CA2499700C (en) | Part inventory management system | |
US10601658B2 (en) | Maintenance of consumable physical components of a network | |
CN101185065A (en) | Method and tool for optimized system maintenance | |
JP2010244200A (en) | Production order replanning system, production order replanning device, and method | |
US11307557B2 (en) | Method for eliminating process anomalies | |
JP4229892B2 (en) | Process management apparatus and process management method | |
CN111292047B (en) | Automobile part source tracing method and system based on Internet of things and block chain | |
US20130332233A1 (en) | Prediction system and program for parts shipment quantity | |
Andrejić et al. | Logistics failures in distribution process | |
Bottani et al. | Inventory management in the presence of inventory inaccuracies: an economic analysis by discrete-event simulation | |
EP3913558B1 (en) | Methods and systems of conveying data to and from a transport climate control system | |
CN104486431A (en) | Method, device and system for monitoring terminal | |
US7409363B2 (en) | Centralized management system for maintenance parts | |
KR20130089838A (en) | Collaborative facilities production system | |
Moskowitz et al. | Allocation of quality improvement targets based on investments in learning | |
CN109190971B (en) | Channel conflict management and control method and system | |
KR101730931B1 (en) | Method and system for managing orders of chemical | |
JP2009042810A (en) | Efficiency improvement support method for supply chain | |
US9952587B2 (en) | Method and system for assembly process |
Legal Events
Date | Code | Title | Description |
---|---|---|---|
AS | Assignment |
Owner name: GM GLOBAL TECHNOLOGY OPERATIONS, INC., MICHIGAN Free format text: ASSIGNMENT OF ASSIGNORS INTEREST;ASSIGNORS:WU, PEILING;TEW, JEFFREY;BILLER, STEPHAN R.;REEL/FRAME:017412/0894;SIGNING DATES FROM 20060119 TO 20060123 Owner name: GM GLOBAL TECHNOLOGY OPERATIONS, INC., MICHIGAN Free format text: ASSIGNMENT OF ASSIGNORS INTEREST;ASSIGNORS:WU, PEILING;TEW, JEFFREY;BILLER, STEPHAN R.;SIGNING DATES FROM 20060119 TO 20060123;REEL/FRAME:017412/0894 |
|
AS | Assignment |
Owner name: UNITED STATES DEPARTMENT OF THE TREASURY, DISTRICT Free format text: SECURITY AGREEMENT;ASSIGNOR:GM GLOBAL TECHNOLOGY OPERATIONS, INC.;REEL/FRAME:022201/0448 Effective date: 20081231 Owner name: UNITED STATES DEPARTMENT OF THE TREASURY,DISTRICT Free format text: SECURITY AGREEMENT;ASSIGNOR:GM GLOBAL TECHNOLOGY OPERATIONS, INC.;REEL/FRAME:022201/0448 Effective date: 20081231 |
|
AS | Assignment |
Owner name: CITICORP USA, INC. AS AGENT FOR BANK PRIORITY SECU Free format text: SECURITY AGREEMENT;ASSIGNOR:GM GLOBAL TECHNOLOGY OPERATIONS, INC.;REEL/FRAME:022553/0493 Effective date: 20090409 Owner name: CITICORP USA, INC. AS AGENT FOR HEDGE PRIORITY SEC Free format text: SECURITY AGREEMENT;ASSIGNOR:GM GLOBAL TECHNOLOGY OPERATIONS, INC.;REEL/FRAME:022553/0493 Effective date: 20090409 |
|
AS | Assignment |
Owner name: GM GLOBAL TECHNOLOGY OPERATIONS, INC., MICHIGAN Free format text: RELEASE BY SECURED PARTY;ASSIGNOR:UNITED STATES DEPARTMENT OF THE TREASURY;REEL/FRAME:023124/0519 Effective date: 20090709 Owner name: GM GLOBAL TECHNOLOGY OPERATIONS, INC.,MICHIGAN Free format text: RELEASE BY SECURED PARTY;ASSIGNOR:UNITED STATES DEPARTMENT OF THE TREASURY;REEL/FRAME:023124/0519 Effective date: 20090709 |
|
AS | Assignment |
Owner name: GM GLOBAL TECHNOLOGY OPERATIONS, INC., MICHIGAN Free format text: RELEASE BY SECURED PARTY;ASSIGNORS:CITICORP USA, INC. AS AGENT FOR BANK PRIORITY SECURED PARTIES;CITICORP USA, INC. AS AGENT FOR HEDGE PRIORITY SECURED PARTIES;REEL/FRAME:023127/0402 Effective date: 20090814 Owner name: GM GLOBAL TECHNOLOGY OPERATIONS, INC.,MICHIGAN Free format text: RELEASE BY SECURED PARTY;ASSIGNORS:CITICORP USA, INC. AS AGENT FOR BANK PRIORITY SECURED PARTIES;CITICORP USA, INC. AS AGENT FOR HEDGE PRIORITY SECURED PARTIES;REEL/FRAME:023127/0402 Effective date: 20090814 |
|
AS | Assignment |
Owner name: UNITED STATES DEPARTMENT OF THE TREASURY, DISTRICT Free format text: SECURITY AGREEMENT;ASSIGNOR:GM GLOBAL TECHNOLOGY OPERATIONS, INC.;REEL/FRAME:023156/0142 Effective date: 20090710 Owner name: UNITED STATES DEPARTMENT OF THE TREASURY,DISTRICT Free format text: SECURITY AGREEMENT;ASSIGNOR:GM GLOBAL TECHNOLOGY OPERATIONS, INC.;REEL/FRAME:023156/0142 Effective date: 20090710 |
|
AS | Assignment |
Owner name: UAW RETIREE MEDICAL BENEFITS TRUST, MICHIGAN Free format text: SECURITY AGREEMENT;ASSIGNOR:GM GLOBAL TECHNOLOGY OPERATIONS, INC.;REEL/FRAME:023162/0093 Effective date: 20090710 Owner name: UAW RETIREE MEDICAL BENEFITS TRUST,MICHIGAN Free format text: SECURITY AGREEMENT;ASSIGNOR:GM GLOBAL TECHNOLOGY OPERATIONS, INC.;REEL/FRAME:023162/0093 Effective date: 20090710 |
|
AS | Assignment |
Owner name: GM GLOBAL TECHNOLOGY OPERATIONS, INC., MICHIGAN Free format text: RELEASE BY SECURED PARTY;ASSIGNOR:UNITED STATES DEPARTMENT OF THE TREASURY;REEL/FRAME:025245/0587 Effective date: 20100420 |
|
AS | Assignment |
Owner name: GM GLOBAL TECHNOLOGY OPERATIONS, INC., MICHIGAN Free format text: RELEASE BY SECURED PARTY;ASSIGNOR:UAW RETIREE MEDICAL BENEFITS TRUST;REEL/FRAME:025314/0901 Effective date: 20101026 |
|
AS | Assignment |
Owner name: WILMINGTON TRUST COMPANY, DELAWARE Free format text: SECURITY AGREEMENT;ASSIGNOR:GM GLOBAL TECHNOLOGY OPERATIONS, INC.;REEL/FRAME:025327/0041 Effective date: 20101027 |
|
AS | Assignment |
Owner name: GM GLOBAL TECHNOLOGY OPERATIONS LLC, MICHIGAN Free format text: CHANGE OF NAME;ASSIGNOR:GM GLOBAL TECHNOLOGY OPERATIONS, INC.;REEL/FRAME:025781/0001 Effective date: 20101202 |
|
STCF | Information on status: patent grant |
Free format text: PATENTED CASE |
|
AS | Assignment |
Owner name: GM GLOBAL TECHNOLOGY OPERATIONS LLC, MICHIGAN Free format text: RELEASE BY SECURED PARTY;ASSIGNOR:WILMINGTON TRUST COMPANY;REEL/FRAME:034184/0001 Effective date: 20141017 |
|
FPAY | Fee payment |
Year of fee payment: 4 |
|
FEPP | Fee payment procedure |
Free format text: MAINTENANCE FEE REMINDER MAILED (ORIGINAL EVENT CODE: REM.); ENTITY STATUS OF PATENT OWNER: LARGE ENTITY |
|
LAPS | Lapse for failure to pay maintenance fees |
Free format text: PATENT EXPIRED FOR FAILURE TO PAY MAINTENANCE FEES (ORIGINAL EVENT CODE: EXP.); ENTITY STATUS OF PATENT OWNER: LARGE ENTITY |
|
STCH | Information on status: patent discontinuation |
Free format text: PATENT EXPIRED DUE TO NONPAYMENT OF MAINTENANCE FEES UNDER 37 CFR 1.362 |
|
FP | Lapsed due to failure to pay maintenance fee |
Effective date: 20210625 |